Forest Snow Depth Estimation Based on Optimized Features and DNN Network Using C-band SAR Data

With the rapid development of remote sensing technology, synthetic aperture radar (SAR) is gradually widely used in snow depth (SD) estimation and serves as a useful complement to optical sensor and passive microwave (PM) sensor for snow remote sensing applications. The selection of parameters and models related to the characteristics of snow in forest is crucial for improving the accuracy of forest SD estimation. The purpose of this letter is to develop a forest SD estimation algorithm based on optimized feature filtering (FF) and deep neural network (DNN) using Sentinel-1 C-band data and other auxiliary data. First, the optimized features are selected from the input dataset using the three FF methods based on machine learning (ML), maximum mutual information coefficient (MMIC), and the proposed Pearson correlation coefficient (PCC). Then, a nonlinear regression method based on DNN was developed to retrieve SD with the optimized features. Comparing results obtained from random forest (RF) algorithm, XGBoost (XGB) algorithm, and recurrent neural network (RNN) algorithm against the meteorological stations and field measured SD data in the forests of Northeast China, the proposed method performs superior with reduced uncertainties. The mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R-2 ) of the proposed SD estimation method are 2.98 cm, 4.30 cm, and 0.77 for the test dataset, respectively.